The Use of Administrative Databases to Assess Oral Health Care
Bibliographic record
Abstract
OBJECTIVE: We examined the potential for research using administrative databases containing dentists' claims to identify both the type of health services research questions addressed and the strength of the evidence that is achieved in such studies. METHODS: We searched Medline (1966 to March, 2003), retrieved additional reports from personal files, reviewed the literature cited in the relevant articles and conducted electronic searches on investigators' surnames. Information from relevant articles was abstracted into tables and the strength of the evidence for each was classified. RESULTS: Thirty-eight studies met our inclusion criteria. Researchers have used administrative databases of dental records to examine provider practices, the longevity or consequences of dental interventions, the prevalence of dental conditions, and patient factors that determined care, and to establish quality assurance criteria or standards of care. The strongest designs were prospective or case-control (Level II-2). CONCLUSION: Studies analyzing administrative databases have the advantage of size and economy but are subject to several threats to their validity and are seldom population-based. The strongest designs occurred with investigation of the longevity or consequences of care. Several studies demonstrated the benefit of linking the service data to patient or provider characteristics. The study of dentists' claims data appears under exploited, especially in the area of identifying and recommending changes in dental health care policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.032 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".